initial draft

src/utils/config_validator.py

+import logging
+from typing import Dict, Any, List
+import os
+
+logger = logging.getLogger(__name__)
+
+class ConfigValidator:
+    """설정 파일 검증"""
+    
+    @staticmethod
+    def validate_evaluation_config(config: Dict[str, Any]) -> List[str]:
+        """평가 설정 검증"""
+        errors = []
+        
+        # 임계값 검증
+        thresholds = config.get('EVALUATION_THRESHOLDS', {})
+        for metric, threshold in thresholds.items():
+            if not isinstance(threshold, (int, float)):
+                errors.append(f"Invalid threshold type for {metric}: {type(threshold)}")
+            elif threshold <= 0:
+                errors.append(f"Invalid threshold value for {metric}: {threshold}")
+        
+        # 가중치 검증
+        weights = config.get('METRIC_WEIGHTS', {})
+        total_weight = sum(weights.values())
+        if abs(total_weight - 1.0) > 0.01:
+            errors.append(f"Metric weights sum to {total_weight}, should be 1.0")
+        
+        # 파일 경로 검증
+        data_paths = config.get('DATA_PATHS', {})
+        for path_name, path_value in data_paths.items():
+            if not os.path.exists(path_value):
+                errors.append(f"Path does not exist for {path_name}: {path_value}")
+        
+        return errors
+    
+    @staticmethod
+    def validate_and_fix_config(config: Dict[str, Any]) -> Dict[str, Any]:
+        """설정 검증 및 자동 수정"""
+        errors = ConfigValidator.validate_evaluation_config(config)
+        
+        if errors:
+            logger.warning(f"Config validation errors found: {errors}")
+            
+            # 자동 수정 시도
+            fixed_config = config.copy()
+            
+            # 임계값 자동 조정
+            if 'EVALUATION_THRESHOLDS' in fixed_config:
+                for metric, threshold in fixed_config['EVALUATION_THRESHOLDS'].items():
+                    if threshold <= 0:
+                        # 기본값으로 설정
+                        default_thresholds = {
+                            'chamfer_distance': 0.1,
+                            'emd': 0.15,
+                            'class_accuracy': 0.8
+                        }
+                        fixed_config['EVALUATION_THRESHOLDS'][metric] = default_thresholds.get(metric, 0.1)
+                        logger.info(f"Auto-fixed threshold for {metric}")
+        
+        return fixed_config